用多模型融合提升胶囊内镜异常分类准确率
Multi-Class Abnormality Classification Task in Video Capsule Endoscopy
- 从CNN到ViT逐步优化，融合空间与通道注意力机制
- 验证集平衡准确率达85.92%，平均AUC达99.32%
- 适合医学影像智能诊断研究者参考
本工作针对2024年胶囊视觉挑战赛中的多类别异常分类任务，采用多种深度学习模型（从自定义CNN到先进Transformer架构）处理视频胶囊内镜（VCE）数据。目标是准确识别多种胃肠道疾病，以提高临床诊断效率。初始使用基础CNN模型，随后通过ResNet增强特征提取能力，再引入Vision Transformer（ViT）捕捉全局依赖关系。进一步采用多尺度视觉变压器（MViT）实现分层特征提取，最终结合空间与通道注意力的双注意力视觉变压器（DaViT）表现最佳。在验证集上，最高平衡准确率为0.8592，平均AUC为0.9932，显著优于其他方法。此外，团队在测试集上获得平均AUC 0.7314、平衡准确率0.3235，排名第七。
原文摘要 · Abstract (English)
In this work for Capsule Vision Challenge 2024, we addressed the challenge of multiclass anomaly classification in video capsule Endoscopy (VCE)[1] with a variety of deep learning models, ranging from custom CNNs to advanced transformer architectures. The purpose is to correctly classify diverse gastrointestinal disorders, which is critical for increasing diagnostic efficiency in clinical settings. We started with a baseline CNN model and improved performance with ResNet[2] for better feature extraction, followed by Vision Transformer (ViT)[3] to capture global dependencies. We further improve the results by using Multiscale Vision Transformer (MViT)[4] for improved hierarchical feature extraction, while Dual Attention Vision Transformer (DaViT) [5] delivered best results by combining spatial and channel attention methods. Our best balanced accuracy on validation set [6] was 0.8592 and Mean AUC was 0.9932. This methodology enabled us to improve model accuracy across a wide range of criteria, greatly surpassing all other methods.Additionally, our team capsule commandos achieved 7th place ranking with a test set[7] performance of Mean AUC: 0.7314 and balanced accuracy: 0.3235
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